The main goal of this work is to introduce a BERT-based entity similarity to capture latent entity type information which is supplementary to existing SOTA local context model [1]}. So we evaluate the performance when integrating the BERT-based entity similarity into the local context model of [1]} ganea2017deep. We also evaluate our model with or without global modeling method of [1]} ganea2017deep.
In addition, we further compare our methods with other state-of-the-art models [4]}, [5]}.
To verify the contribution of our proposed BERT-based entity embeddings, we also compare with a straightforward baseline which directly replaces the encoder of [1]} ganea2017deep utilizing pre-trained BERT. To do so, we introduce a \(768 \times 300\)  dimensional matrix \({\mathbf {W}}\)  which projects BERT-based context representation \(\mathbf {c}\)  into [1]} ganea2017deep's entity embeddings space when calculating the similarity score.
